library(dplyr)
library(plotly)For this assignments a number of data sets are available at https://data.fivethirtyeight.com/.
Based on my interest in politics, I selected https://fivethirtyeight.com/features/the-big-lie-voting-laws/, which contains data on legislative measures for major electoral change.
Data for this data set is publicly available as a google spreadsheet. Working directly with the google spreadsheet involved OAuth and was cumbersome, so I downloaded the google spreadsheet as a csv file. After downloading the spreadsheet, next step was loading it into R:
ds <- read.table(file="The Big Lie's Long Shadow - Sheet1.csv", header=TRUE, sep=",")Summary shows the data set has 579 rows and 5 columns:
summary(ds)## State Bill Introducing.Party Category
## Length:579 Length:579 Length:579 Length:579
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
## Status
## Length:579
## Class :character
## Mode :characterStatus of the proposed legislative measure tells us whether the proposed legislative measure passed (or not), was signed into law (or not), etc. Notice that Status is a character data type.
Let’s convert data type from character to factor. This makes it easier to analyze Status. For example, notice the summary of modified data set shows factor counts. The counts show that for a majority of measure the legislature adjourned without passing. Out of 579 measures, 50 were signed into law.
ds <- ds %>% mutate(Status = as.factor(Status))
summary(ds)## State Bill Introducing.Party Category
## Length:579 Length:579 Length:579 Length:579
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Status
## Legislature adjourned without passing :283
## Referred to committee : 96
## Signed into law : 50
## Passed committee, legislature adjourned without passing: 41
## Passed Senate, House adjourned without passing : 15
## Passed House, Senate adjourned without passing : 13
## (Other) : 81Now, let’s change the Introducing Party values as follows: D: Democrat, R: Republican, Mostly D: Majority Democrat, Mostly R: Majority Republican:
ds$Introducing.Party[ds$Introducing.Party == "D"] <- 'Democrat'
ds$Introducing.Party[ds$Introducing.Party == "R"] <- 'Republican'
ds$Introducing.Party[ds$Introducing.Party == "Mostly D"] <- 'Mostly Democrat'
ds$Introducing.Party[ds$Introducing.Party == "Mostly R"] <- 'Mostly Republican'Let’s make sure column values were indeed changed by looking at unique values.
unique(ds$Introducing.Party)## [1] "Democrat" "Republican" "N/A"
## [4] "Mostly Democrat" "Mostly Republican" "Bipartisan"Let’s take a look at the complete summary of Status. There are 30 unique nominal values (a lot). Clearly, measures traverse a long journey from being introduced to being passed: they may (may not) pass the House or the Senate, may (may not) be signed into law:
summary(ds$Status)## Became AB 6970
## 1
## Became AB 7478
## 1
## Became HB 1498, HB 1499, HB 1501, and HB 1502
## 1
## Became HB 2905
## 1
## Became SB 1819
## 1
## Became SB 2545
## 1
## Became SB 264
## 1
## Died in House
## 1
## Failed to cross over
## 3
## Legislature adjourned without passing
## 283
## Never introduced
## 1
## Passed committee
## 7
## Passed committee, legislature adjourned without passing
## 41
## Passed House
## 5
## Passed House, died in Senate
## 5
## Passed House, Senate adjourned without passing
## 13
## Passed Senate
## 11
## Passed Senate & House, then died
## 3
## Passed Senate, died in House
## 6
## Passed Senate, House adjourned without passing
## 15
## Passed Senate; House adjourned without passing
## 1
## Referred to committee
## 96
## Rejected by committee
## 1
## Rejected by House
## 3
## Rejected by Senate
## 3
## Signed into law
## 50
## Veto-overriden into law
## 3
## Vetoed
## 13
## Voting restriction removed from bill
## 4
## Withdrawn
## 4A quick plot of Status shows that for a majority of measures either the ‘Legislature adjourned without passing’ or the measure was ‘Referred to committee’:
plot_ly(ds, x=ds$Status)The nominal value Signed into law identifies the legislative initiatives that actually survived the long legislative process and became laws. To make it easy to identify and work with measures that became law, we do the following trtansformations:
ds <- ds %>% mutate('Signed Into Law' = (Status == 'Signed into law'))Final data set:
summary(ds)## State Bill Introducing.Party Category
## Length:579 Length:579 Length:579 Length:579
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Status Signed Into Law
## Legislature adjourned without passing :283 Mode :logical
## Referred to committee : 96 FALSE:529
## Signed into law : 50 TRUE :50
## Passed committee, legislature adjourned without passing: 41
## Passed Senate, House adjourned without passing : 15
## Passed House, Senate adjourned without passing : 13
## (Other) : 81Impact of the ‘Big Lie’ on legislative measures for the electoral process is a highly relevant topic. One way to extend this analysis would be to collate census data into the study to see what percentage of the population is impacted by the proposed legislature. It would also be relevant to study correlations between proposed measures and ‘swing’ states’.